258: CHALLENGES IN INSTITUTIONAL APPROVAL PROCESS FOR A MULTINATIONAL STUDY: THE GLOBAL PARITY
Bibliographic record
Abstract
Introduction: Multicenter, multinational collaborative research has increased across differing resource capacities by country. Inconsistencies in ethical review processes is an under recognized resource limitation and in resource-limited settings across regions may lead to barriers to rapid deployment of important clinical studies. Methods: An ancillary prospective study of the global PARITY (pediatric acute critical illness study) point prevalence study of pediatric critical illness in resource-limited settings. We surveyed participating sites to explore ethical and board review processes. The aim of the study was to describe the time, costs, and barriers in obtaining institutional review board and ethics approval for a multinational, multicenter, non-interventional study in critically ill children involving low and middle-income countries. Results: Fifty-six centers completed the survey. Of those hospitals, 72% (n= 40) required a full board review of the global PARITY study prior to approval, and 17% (n=9) accepted an ethics approval from a separate institution. The frequency of institutional review board (irb) committee is variable between centers, 16% (n=9) weekly, 56% (n=31) meet every 1 – 2 month, 11% (n=6) every 3 – 6 month and in 16% (n=9) > six months. The IRB approval process took a median of 62 days (n=40; range 1-294 days). Sixteen centers (30%) reported costs associated with ethical review. The waived consent was approved by 71% (n=38) of the centers, and 23% (n=12) required a data sharing agreement. The protocol required translation to local language in 47% (n=26) centers, from which 25% (n=6) required a professional certificated translation. Of the centers surveyed, 32% (n=17) required a legal review of protocol or data sharing agreement. Conclusions: There is great variability in ethical review requirements and time of approval for a non-interventional multinational study of critically ill children in low and middle-income countries in both private and public hospitals. We show that this time resource must be considered when planning multinational studies. Enhancing the capacity for ethics and board review process across regions will allow for better matching of disease burden with clinical research and likely increase participation in this multicenter research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.561 | 0.465 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".